I am new to Azure ML studio and trying to run python script.
Currently I am working on Text analytics related code and as part of that I want to get the singular values of SVD decomposition something like below
lsa=TruncatedSVD(algorithm='randomized',n_components=MaximumNumComponents,n_iter=20,random_state=42,tol=0.0)
U = lsa.fit_transform(X)
Sigma = lsa.singular_values_
Current version of scikit learn in Azure ML studio is 0.17 and singular values is part of higher versions of scikit learn like 0.20.
So I need to upgrade scikit learn package to 0.20. I tried downloading scikit learn 0.20 wheel file and zipped it and uploaded as dataset into Azure ML studio and connected enter image description here but still getting error like below "
AttributeError: 'TruncatedSVD' object has no attribute 'singular_values_'
Process returned with non-zero exit code 1
Already referred to below questions as well
Stackoverflow questions related to upgrade packages in Azure ML studio
How can I install Python packages in Azure ML?
Currently this is the limitation with Azure ML studio. The only way is to reach out to support who generates the necessary package and we can upload it to Azure ML Studio.
Related
I'm using the Python Sandbox in Azure Data Explorer to do inference on my data tables.
However, in some of my python code I'll need to upgrade the python() sandbox. (e.g., my models are TensorFlow 2 models instead of TF 1 provided by Py 3.6 and Anaconda 5.2 inside the sandbox).
I was looking online but didn't find any good solution on how to upgrade Azure Data Explorer Python Sandbox Anaconda and Python Version
I've tried to follow the documentation on Azure Data Explorer Python Sandbox Policy, but there's no mention on upgrading the Python and Anaconda version
Another information that I've found is the dependencies versions are shown in the Anaconda page
If anyone knows of a solution or any information to run this python sandbox upgrade that'd be really great
Or if it's not possible can anyone suggest me what other Azure solution I might use to do inference on my data tables?
Thanks and have a great day!
There is a new version based on Python 3.10.8 + latest packages. This version is still in preview, in few weeks it would be GA. Currently this upgrade is not yet self service, you should contact ADX support to perform the upgrade. You can also email me (adieldar#microsoft.com), specifying your cluster name, and I can take it from there.
thanks,
Adi
I know you need azure for vs2019 image recognition but does vs2022 also use azure. I cant find any documentation on this
The same vs2019 object recognition tutorial do work for vs2022.
Here is a sample tutorial you can refer to which works for vs2019 or later.
As an alternative Image Analysis can be also done in Microsoft Computer Vision algorithms that can analyze visual content in different ways based on inputs and user choices.
I would like to create a docker image based on the databricks runtime for machine learning and extend it further.
The base runtime i can simply extend in a dockerfile by doing:
FROM databricksruntime/standard:9.x
Can i do something similar for the ML runtime?
As a follow-up can I see the source code of the ml runtime?
Cheers
ML Runtime = Standard Runtime + ML Libs.
Databricks Runtime 10.2 ML is built on top of Databricks Runtime 10.2.
Reference
You can recreate it by installing the libraries mentioned here.
ver 10.2 is used as an example in this answer.
I am using the Azure Automated Machine Learning SDK to train a machine learning model on my dataset. However, after the experiment, all my training iterations fail with a KeyError: 'brand' error even if the model training itself succeeded.
How can I resolve this?
If a new environment was created after 10 June 2020 using SDK 1.7.0 or lower, training may fail with the above error due to an update in the py-cpuinfo package. (Environments created on or before 10 June 2020 are unaffected, as well as experiments run on remote compute as cached training images are used.) To work around this issue, either of the two following steps can be taken:
Update the SDK version to 1.8.0 or higher (this will also downgrade py-cpuinfo to 5.0.0):
pip install --upgrade azureml-sdk[automl]
Downgrade the installed version of py-cpuinfo to 5.0.0:
pip install py-cpuinfo==5.0.0
I have been working on machine learning problems lately as part of my internship. So far I have been using Tensorflow with python because that's what I am most comfortable with. Once a problem is solved using deep learning, I am left with the architecture of the network and the weights. Now, my problem is, how can I deploy my solution in production? I won't be using tensorflow serving because it is mainly for huge applications where you set a remote server and your developed application will make requests to this server. In my case, I just want to develop a machine learning solution and integrate it into an already existing software that uses C++ with visual studio 2017.
So far and after a lot of research, I have some solutions in mind :
1) Using the "dnn" module from OpenCV : this module can load graphs and you can do inference and other operations (like extracting a specific layer from the network at run time). This module seemed very promising but then I started facing some problems when using networks that are a little bit different from the one used in the example described in OpenCV github, they used "inception5h" for the example and when I tried to load "inception_v3" there was an error about some unknown layer in the network, namely the JPEG_decode layer.
2) Building tensorflow from source and using it directly with C++. This solution seemed like the best one but then I encountered so many problems with parts of my code not compiling while others do. I am using Visual Studio 2017 with Windows 10. So although I was able to build tensorflow from source, I wasn't able to compile all parts of my code, in fact it wasn't even my code, it was an example from tensorflow website, this one : tensorflow C++ example.
3) Another possibility that I am entertaining is using tensorflow for designing the solution and then using another machine learning framework such as Caffe2, CNTK...etc for deployment into production. I have found some possibilities to convert graphs from one framework to another here : models converters. I thought that this could be a reasonable solution because all I have to do is find the framework most compatible with windows and just do a model conversion once I finish designing my solution in tensorflow and python. The conversion process though seems a little too good, am I wrong?
4) A final possibility that I am thinking of is using CPython. So basically, I will create my the pipeline for prediction in python, wrap in some python functions then use <Python.h> in my Visual Studio project and make calls to those functions using C++, here's an example : embedding python in C++. I have never used a solution like this before and I am not sure about all the things that could go wrong.
So basically, what do you think is the best solution to deploy a machine learning solution into an already existing project on Visual Studio that uses C++? Am I missing a better solution? Any guidelines or hints are greatly appreciated!
I ended up using solution 2. After the new updates from tensorflow, it's now easier to build tensorflow from source on Windows. With this solution, I didn't need to worry about the compatibility of my models since I use tensorflow with python for prototyping and I use it with C++ for production.
[EDIT] : In 2021, I am now using ONNX Runtime (ORT) for deploying my models in production as part of a C++ application. The documentation for ORT is not great but the tool itself is very good.
I used CNTK from the beginning because I just wanted to stay in my C++ world in Visual Studio, and knew that I wanted to deploy as part of my C++ desktop App. No Tensorflow, no Python, no cloud, not even .NET, and no translating models. Just do it in CNTK from the start. I have a commercial product now using Deep Learning. Cool!
I'd consider exporting your NN model (which is not restricted to tensorflow) using ONNX to Intel Vino or TensorRT in order to export your model to C++ for optimized CPU or optimized GPU
It's states here that Intel Vino is twice as fast as tensorflow